The PhD defence and trial lecture will be held in Auditorium 1 in The Geology Building. In some cases, it will be possible to attend the trial lecture and dissertation digitally, in that case a link to Zoom will be posted.
Trial lecture
Wednesday 18 October, 10:15-11:00, Aud 1, The Geology Building
Machine learning in hydrometeorological forecasting
Conferral summary (in Norwegian)
Ekstremværet «Hans» har vist at ekstremnedbør kan ramme kritiske samfunnsområder, slik som infrastruktur, transport, landbruk, menneskers liv og verdier. Det er derfor nødvendig å forstå ekstreme nedbørshendelser for å kunne forutsi sannsynlighet, frekvens og for å gi et pålitelig dimensjoneringsgrunnlag. Denne avhandlingen fokuserer på ekstremnedbør i Norge, og det foreslås en ny metodikk for å beregne dimensjonerende verdier for ekstremnedbør. Som konklusjon foreslås det å kombinere et modellbasert sesongvarslings-system med gridbaserte observasjonsdata for nedbør, for å oppnå pålitelige returverdier, også for de nedbørhendelsene som ventes å skje veldig sjeldent.
Main research findings
Popular scientific article about Ødemark’s dissertation:
Extreme precipitation and physical model-based estimates for return values and PMP in Norway
The rise in extreme rainfall and subsequent flooding has become an increasing concern in our changing climate. This challenge impacts various aspects of our society, including infrastructure, agriculture, and safety. To address this, it is essential to understand extreme rainfall events, predict their likelihood and frequency, and estimate robust design values used to plan and design infrastructure. This is particularly true for high-risk facilities like dams and nuclear power plants, where design values must account for rare, but catastrophic events. At the core of this effort lie the concepts of extreme precipitation statistics and design values determined through return value estimates..
To secure water infrastructure, engineers often rely on a concept known as "Probable Maximum Precipitation" (PMP), which represents the maximum possible amount of rainfall under specific conditions. In Norway, the methodology for estimating PMP is based on an approach developed in the 1970s, heavily reliant on historical weather data and subjective choices. This doctoral work focuses on extreme precipitation in Norway and explores alternative methods for the PMP estimation. The study proposes to combine data from a weather prediction system with a gridded observation data set to improve the accuracy and reliability of the estimates, enhancing our preparedness for exceptionally heavy rainfall and ensuring the safety of critical infrastructure.
Photo and other information:
Press photo: Karianne Ødemark, portrait; 1200px. Photo: Magne Velle
